README.md

---
license: apache-2.0
language:
- zh
- en
- es
- fr
- de
- ru
- ja
- ko
base_model:
- Qwen/Qwen3-Embedding-4B
library_name: transformers
---
# Querit-Reranker-4B

## HighLights

Querit-Reranker-4B is a multilingual cross-encoder reranker initialized from Qwen3-Embedding-4B and further trained with a data-centric, reranking-oriented pipeline. Rather than relying on backbone scale alone, the model first learns broad query-document relevance matching from large-scale ranking supervision and then adapts to target ranking distributions through synthetic-query mining with teacher scores as continuous soft labels.

Selected checkpoints from different data mixtures and training runs are further consolidated with spherical linear interpolation (SLERP), yielding a single deployable reranker without runtime ensembling overhead. By jointly encoding each query-document pair, Querit-Reranker-4B captures fine-grained relevance signals for second-stage ranking and achieves strong performance across multilingual and English retrieval benchmarks.

As of June 20, 2026, Querit-Reranker-4B achieves the best average score of **71.09** among publicly available models on the **MTEB Multilingual v2 reranking tasks**, averaged over six tasks.
![Reranking performance on MTEB-multilingual-v2](./MTEB-multilingual-v2.png)



### Model Description

<!-- Provide a longer summary of what this model is. -->


- **Model type:** Text Reranking
- **Language(s) (NLP):** Multilingual (Chinese, English, Spanish, French, German, Russian, Korean, Japanese)
- **Training Stage:** Pretraining & Post-training
- **Number of Total Parameters:** 4.02B
- **Number of Paramaters (Non-Embedding):** 3.63B
- **Number of Layers:** 36
- **Number of Attention Heads:** 32
- **Context Length:** 128k

## Citation

If you find Querit-Reranker useful for your research or applications, please cite our paper:

**Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation**
Yunfei Zhong, Jun Yang, Wei Huang, Yinqiong Cai, Haosheng Qian, Yixing Fan, Ruqing Zhang, Lixin Su, Daiting Shi, and Jiafeng Guo.
arXiv:2606.19037, 2026.

```bibtex
@misc{zhong2026queritrerankertrainingcompactmultilingual,
      title={Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation}, 
      author={Yunfei Zhong and Jun Yang and Wei Huang and Yinqiong Cai and Haosheng Qian and Yixing Fan and Ruqing Zhang and Lixin Su and Daiting Shi and Jiafeng Guo},
      year={2026},
      eprint={2606.19037},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2606.19037}, 
}
```